Cyber-physical systems (CPS) are challenging to control due to the complex uncertainties arising from physical and virtual sources. Enhancing CPS with self-adaptation is beneficial in addressing these uncertainties. While reactive adaptation often struggles with reliability, proactive adaptation could be more advantageous by preparing systems to make informed decisions, considering the consequences of changes before they occur. CPS and their execution environment usually exhibit timevarying or non-linear dynamics, which are more complex to predict than linear systems, while recent proposals of proactive self-adaptation methods have focused on linear systems. This work bridges this gap by proposing a method for Proactive self-Adaptation for Nonlinear Cyber-physical Systems (PANCS). PANCS is developed through a ground vehicle running example, leveraging MAPE-K loop, and its strengths and limitations are discussed.